DomusFM: 一个面向智能家居传感器数据的基础模型
DomusFM: A Foundation Model for Event-Based Behavioral Monitoring in Smart-Homes
AI总结：
DomusFM是首个专为智能家居传感器数据设计的基础模型，通过自监督双对比学习范式实现语义和时间依赖性的建模，有效提升活动识别任务的性能。
摘要：
智能家居传感器数据在多个应用中具有重要潜力，包括健康监测和辅助技术。然而，现有方法面临关键限制。监督模型需要不切实际的大量标记数据。针对活动识别的基础模型仅关注惯性传感器，未能解决智能家居二进制传感器事件的独特特性：其稀疏、离散的性质与丰富的语义关联。虽然LLM方法在该领域已得到测试，但仍然存在几个问题，即需要自然语言描述或提示，以及依赖外部服务或昂贵硬件，这使得在实际场景中由于隐私和成本问题而不可行。我们引入了DomusFM，这是首个专门设计和预训练用于智能家居传感器数据的基础模型。DomusFM采用自监督双对比学习范式，以捕捉令牌级语义属性和序列级时间依赖性。通过整合轻量级语言模型的语义嵌入和专门用于时间模式和二进制状态的编码器，DomusFM学习了可泛化表示，这些表示能够在活动和事件分析相关环境和任务中转移。通过七种公开智能家居数据集的leave-one-dataset-out评估，我们证明DomusFM在不同下游任务上优于最先进的基线，即使在只有5%标记训练数据可用进行微调时也表现出色。我们的方法解决了数据稀缺问题，同时保持了实际部署的可行性。
英文摘要：
Smart-home sensor-based behavioral monitoring holds significant potential for healthcare, independent living, and early detection of functional or cognitive changes. In this setting, tasks like activity recognition, prediction, and pattern discovery provide complementary views of daily life, supporting the modeling of personal routines and habits, and their long-term changes. Existing approaches, however, face critical limitations. Supervised approaches require impractical amounts of labeled activity data to capture the variability of daily behavior across residents and environments. Foundation models represent a promising direction for learning transferable representations of latent behavioral patterns from sensor data. Still, current efforts are mostly designed for continuous inertial or physiological sensor data and do not address the sparse, discrete, and semantically rich event streams produced by smart homes. In this paper, we introduce DomusFM, a domain-specific foundation model for sensor-based behavioral monitoring in smart homes based on semantic event streams. DomusFM employs a self-supervised dual contrastive learning paradigm to capture both event-level semantic attributes and sequence-level temporal dependencies. By integrating semantic embeddings from a lightweight language model and specialized encoders for temporal patterns and binary states, DomusFM learns transferable representations that can be adapted across heterogeneous smart-home environments and tasks related to activity and event analysis. Through a leave-one-dataset-out evaluation across seven public smart-home datasets, we demonstrate that DomusFM consistently outperforms baselines on three downstream tasks: ADL recognition, next-k event prediction, and unsupervised clustering. DomusFM has a small footprint and can be deployed on edge devices.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
